AIMC Topic: Machine Learning

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Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning Techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disord...

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.

Computational and mathematical methods in medicine
Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's disease and type II diabetes. Therefore, accurately identifying amyloid is necessary to understand its...

A data driven methodology for social science research with left-behind children as a case study.

PloS one
For decades, traditional correlation analysis and regression models have been used in social science research. However, the development of machine learning algorithms makes it possible to apply machine learning techniques for social science research ...

Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges.

European radiology
Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society o...

Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants.

The Analyst
Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effectiv...

Machine learning for metabolic engineering: A review.

Metabolic engineering
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth i...

Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient grou...

Comparing Machine Learning Models for Aromatase (P450 19A1).

Environmental science & technology
Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition o...

A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System.

IEEE transactions on bio-medical engineering
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wi...

A pitfall for machine learning methods aiming to predict across cell types.

Genome biology
Machine learning models that predict genomic activity are most useful when they make accurate predictions across cell types. Here, we show that when the training and test sets contain the same genomic loci, the resulting model may falsely appear to p...